In a 3rd Generation Partnership Project (3GPP) Long Term Evolution Advanced (LTE-A) system, multiple layer MIMO transmission is adopted to achieve high spectral efficiency, and corresponding receivers are required to detect multiple layer MIMO to obtain demodulated data.
There are many MIMO detection methods. Sphere Decoding (SD) detection can approach the optimal performance, i.e., Maximum Likelihood performance, thus in order to obtain the optimal performance, the SD detection is used to implement MIMO detection in cases of acceptable hardware implementation complexity. The SD detection includes Fixed-complexity Sphere Decoding (FSD) and non fixed-complexity sphere decoding, and the FSD is commonly used since it is readily to be implemented by Very Large Scale Integration (VLSI). In addition, soft-output MIMO detection can be adapted to subsequent soft decoders to enable the system to achieve better detection performance, thus SFSD detection is commonly used at present.
Generally, processes for receivers in an LTE-A system to perform SFSD detection mainly include preprocessing, sphere detection and Likelihood Ratio (LLR) output, wherein the preprocessing includes generation of an equivalent channel matrix, sequencing and QR decomposition.
Due to high requirements on spectral efficiency of a system based on International Mobile Telecommunications-Advanced (IMT-Advanced) specifications established by the International Telecommunications Union (ITU), an LTE-A system is required to achieve a downlink spectral efficiency of 30 bps/Hz and an uplink spectral efficiency of 15 bps/Hz. In order to meet these requirements, an LTE-A system adopts 8-layer MIMO at most on downlink and 4-layer MIMO at most on uplink, and it also supports modulation modes such as Quadrature Phase Shift Keying (QPSK)/16 Quadrature Amplitude Modulation (QAM)/64QAM. Due to a great increase in MIMO layers, a very large amount of computation is resulted in sphere detection.
An article (prior art 1) entitled “A low-complexity soft MIMO detector based on the fixed-complexity sphere decoder” (by L. G. Barbero, T. Ratnarajah, and C. Cowan, published on IEEE International Conference on acoustics, speech and signal processing (ICASSP'08), Las Vegas, USA, March/April 2006) and an article (prior art 2) entitled “Fixing the complexity of the sphere decoder for MIMO detection” (by L. G. Barbero and J. Thompson, published on IEEE Transaction. on Wireless Communications, vol. 7, no. 6, June 2008) proposed a method for reducing complexity of SFSD detection, in which an ML complementary set path is structured using bit-negating, i.e., only a measurement of a path of which each bit is negated in sequence among ML paths is calculated (for example, for 64QAM, each symbol has 6 bits, thus there are only 6 possible complementary set paths), in this way, though the complexity is reduced, too much information is lost when only a bit-negated path is calculated, thus the detection performance cannot reach the optimal ML performance.
An article (prior art 3) entitled “Soft-Output Sphere Decoding: Algorithms and VLSI Implementation” (by Studer, C.; Burg, A.; Bolcskei, H., published on IEEE Journal on Selected Areas in Communications, vol. 26, no. 2, February 2008) proposed a non fixed-complexity sphere encoding method unlike the fixed-complexity encoding in which a fixed node is reserved in each layer and detection will be carried out at a next layer after all possible nodes of a previous layer have been detected, instead, one node of a next layer will be detected after one node of a previous layer has been detected until a bottom layer is reached, then path backtracking is performed according to detection radii and certain criteria until an optimal path is obtained. The optimal ML performance can be achieved in this way, but the complexity is not fixed, and it will be hard to implement the method when a channel is changed to a certain condition where the complexity is extremely high.